Current Signature Research Articles

A Novel High Impedance Fault Detection Strategy for Microgrid Based on Differential Energy Signal of Current Signatures and Entropy Estimation

The non-linearly deterministic fault current characteristics with low current magnitude of high impedance fault (HIF) have thrown an inevitable challenge to the microgrid protection system. Moreover, differentiating HIF from non-fault events is also a challenging task under different operating scenarios of the microgrid. In order to address this, a simple and fast protection algorithm, based on Empirical Mode Decomposition (EMD) in conjugation with Teager-Kaiser Energy Operator (TKEO) is proposed in this article. This scheme process the current signals retrieved from both ends of the line through EMD, and subsequently, the current signal energy is extracted by TKEO. The incidence of a fault is affirmed by measuring the energy difference signal and estimation of entropy. By employing the second layer algorithm using entropy analysis, the non-fault disturbances, such as capacitor and load switching, temporary overloading, and DG outage, have been effectively distinguished from HIFs. The proposed technique is tested for grid-tied and autonomous microgrid operation modes with several HIF and non-fault cases. It is inferred that the proposed technique can detect and differentiate HIFs from other system perturbations seamlessly in less time. Compared to other methods, this scheme has procured excellent accuracy and reliability, and is also not affected by the dynamic current behavior of the microgrid. The PSCAD-EMTDC software is used to build and simulate the test system, and the MATLAB programming interface is utilized to develop and test the suggested method.

Prognosis to Radiation Unlocked: How Hypoxia Methylome May Hold the Key in HNSCC.

Hypoxia in head and neck tumors has proven to be predictive of outcomes. Current hypoxia signatures have failed for patient treatment selection. In a recent study, the authors identified a hypoxia methylation signature as a more robust biomarker in head and neck squamous cell carcinoma and shed light into the mechanism of hypoxia-mediated treatment resistance. See related article by Tawk et al., p. 3051.

Sum of Gaussian Feature-Based Symbolic Representations of Eddy Current Defect Signatures

ABSTRACT This study investigates a novel symbolic representation method based on Symbolic Aggregate Approximation (SAX) of time series that focuses on differential coil (D-coil) eddy current (EC) defect responses. The method uses the Sum of Gaussian (SoG) approximation of the defect response, Fuzzy C-means (FCM) clustering, and the extrema values in the approximation to provide a reduced representation that can effectively analyze possible fault conditions in the defect response. Comparisons to existing SAX methods are performed with the new method indicating significant classification accuracy improvement.

Fault Diagnosis/Separation of Surface Mounted Permanent Magnet Synchronous Machine by Current and its Homopolar Orders Analysis

Performance analysis of Surface Mounted Permanent Magnet Synchronous Machine (SM-PMSM) is investigated in this paper. The machine dynamic in both healthy and faulty cases is analyzed, where dynamic, static, and mixed eccentricities as well as inter-turn and demagnetization faults are considered by a unique model. Stator Current Signature Analysis (SCSA) is used for the fault detection, and a method is proposed for the faults diagnosis and identification. Also, a flexible Magnetic Equivalent Circuit (MEC) with adjustable accuracy is used to model saturation effect thanks to its more flexibility and shorter processing time compared to the Finite-Element Method (FEM). The Power Spectral Density (PSD) is used for the faults detection, where analysis of the homopolar orders is proposed to be able to differentiate various faults from each other. Performed validation by experimental and FEM-based results shows the effectiveness of the presented model and proposed fault detection method.

Visibility of Vegetation High-Impedance Fault Low-Frequency and High-Frequency Signatures

This letter exhibits the presence of High-Frequency (HF) current signatures of arcing in Vegetation High Impedance Fault (VeHIF) events. Zero-crossing arcing discontinuities are shown to have varying widths causing different levels of HF noise bursts depending on how aggressively the 50-Hz waveform gets distorted. The work has identified that the temporal growth in HF disturbances often occurs quicker than those linked to the third harmonic or the entire Low-Frequency (LF) spectrum. In 59.2 % of a dataset of 125 phase-to-earth (ph-to-e) faults, the growth in HF spectrum was identified to occur faster than the third-harmonic or the entire LF spectrum. The HF spectrum had an average lead time of 26.58 s over the LF spectrum. The LF indicator performed better in only 15 tests (12 %) with an average lead time of 9.6 s. In 13 tests (10.4 %), all three indicators concurrently reached the set normalized threshold.

Online condition monitoring system for rotating machine elements using edge computing

ABSTRACT Misalignment, imbalance, induced vibrations, and noise in rotating machines must be identified early on using condition monitoring and signal processing techniques. If it is not detected early, the machine’s reliability will suffer, potentially resulting in a catastrophic failure of the machine components. In this study, a web application for real-time fault detection is designed and built using a novel approach of edge computing and IoT. Vibration signature analysis are used to determine the severity of faults in machine rotating components and to provide an early warning even when the maintenance crew is located in a remote location. The vibration spectrum analysis results are successfully obtained and verified using the vibration-metre tool VibXpert-II. The purpose of this research is to improve real-time condition monitoring of rotating systems like bearings and induction motors and make it available on an online platform for predictive maintenance. Vibration signatures provide more accurate information regarding the type and location of rotor faults than current signatures.

Detection and evaluation of load oscillations in induction motors based on MCSA

Load oscillations of induction motors can be the consequence of electrical or mechanical faults. One of the most important techniques to diagnose load oscillations is the so-called motor current signature analysis (MCSA). It has won widespread popularity owing to its non-intrusive and cost-effective performance. The common practice of MCSA is to detect the characteristic harmonic components of stator current caused by load fluctuations. However, in certain scenarios, the characteristic components can be hardly tracked from the current spectrum. Power frequency interference, nonstationary randomness of the sampled signals, and other factors could all limit the use of classical current spectrum analysis. The presented solution is the joint application of variational mode decomposition (VMD), delay addition method, multiple signal classification (MUSIC), and neural network. Specifically, VMD, delay addition and MUSIC are combined to decompose the raw signals, eliminate the power frequency leakage interference and extract the weak oscillation features with enhanced visualization. Meanwhile, a neural network is generated to describe the statistical features of the processed signals, and further realize the automatic evaluation of the load oscillation severity level. Theoretical analysis and experimental verification have been conducted to support this article. The results suggest that the proposed algorithm contributes to a more sensitive detection of the presence of load oscillations, and a more reliable evaluation of their severity with respect to traditional MCSA techniques.

A senescence-related signature for predicting the prognosis of breast cancer: A bioinformatics analysis.

Breast cancer is a heterogeneous disease with diverse prognosis and treatment outcomes. Current gene signatures for prognostic prediction are limited to specific subtypes of breast cancer. Cellular senescence is a state of irreversible cell cycle arrest that affects various physiological and pathological processes. This study aimed to develop and validate a senescence-related signature for predicting the prognosis of breast cancer patients. We retrieved 744 senescence-associated genes from the SeneQuest database and analyzed their expression profiles in 2 large datasets of breast cancer patients: The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We used univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis to derive a 29-gene senescence-related risk signature. The risk signature was significantly associated with disease-specific survival (DSS), clinical characteristics, molecular subtypes, and immune checkpoint genes expressions in both datasets. The risk signature also stratified high-risk and low-risk patients within the same clinical stage and molecular subtype. The risk signature was an independent prognostic factor for breast cancer patients. The senescence-related signature may be a useful biomarker for predicting prognosis and immunotherapy response of breast cancer patients. The risk signature may also guide adjuvant chemotherapy decisions, especially in hormone receptor positive (HR+) and human epidermal growth factor receptor type 2 (HER2)- subtypes.

Motor Current Signature Analysis for Detecting Local Defects on Rolling-Element Bearings of Induction Motors

Motor Current Signature Analysis for Detecting Local Defects on Rolling-Element Bearings of Induction Motors

CBFISKD: A Combinatorial-Based Fuzzy Inference System for Keylogger Detection

A keylogger is a type of spyware that records keystrokes from the user’s keyboard to steal confidential information. The problems with most keylogger methods are the lack of simulated keylogger patterns, the failure to maintain a database of current keylogger attack signatures, and the selection of an appropriate threshold value for keylogger detection. In this study, a combinatorial-based fuzzy inference system for keylogger detection (CaFISKLD) was developed. CaFISKLD adopted back-to-back combinatorial algorithms to identify anomaly-based systems (ABS) and signature-based systems (SBS). The first combinatorial algorithm used a keylogger signature database to match incoming applications for keylogger detection. In contrast, the second combinatorial algorithm used a normal database to detect keyloggers that were not detected by the first combinatorial algorithm. As simulated patterns, randomly generated ASCII codes were utilized for training and testing the newly designed CaFISKLD. The results showed that the developed CaFISKLD improved the F1 score and accuracy of keylogger detection by 95.5% and 96.543%, respectively. The results also showed a decrease in the false alarm rate based on a threshold value of 12. The novelty of the developed CaFISKLD is based on using a two-level combinatorial algorithm for keylogger detection, using fuzzy logic for keylogger classification, and providing color codes for keylogger detection.

Andreev processes in mesoscopic multiterminal graphene Josephson junctions

There is growing interest in using multi-terminal Josephson junctions (MTJJs) as a platform to artificially emulate topological phases and to investigate complex superconducting mechanisms such as quartet and multiplet Cooper pairings. Current experimental signatures in MTJJs have led to conflicting interpretations of the salient features. In this work, we report a collaborative experimental and theoretical investigation of graphene-based four-terminal Josephson junctions. We observe resonant features in the differential resistance maps that resemble those ascribed to multiplet Cooper pairings. To understand these features, we model our junctions using a circuit network of coupled two-terminal resistively and capacitively shunted junctions (RCSJs). Under appropriate bias current, the model predicts that a current flowing between two diagonal terminals in a four-terminal geometry may be represented as a sinusoidal function of a weighted sum of the superconducting phases. We show that starting from a semi-classical model with diffusive current-phase relations, the MTJJ effectively emulates a general form of the expected current-phase relation for multiplet Cooper pairings. Our study therefore suggests that differential resistance measurements alone are insufficient to conclusively distinguish resonant Andreev reflection processes from semi-classical circuit-network effects.

Innovative Conveyor Belt Monitoring via Current Signals

This paper proposes, investigates, and validates, by comprehensive experiments, new online automatic diagnostic technology for belt conveyor systems based on motor current signature analysis (MCSA). Motor current signature analysis (MCSA) is a method employed for detecting faults in electric motors by analyzing the current waveforms generated during motor operation. The technology capitalizes on the fact that motor defects, such as mechanical misalignment, bearing damage, and rotor bar defects, cause variations in a motor’s current waveforms, which can be discerned and analyzed using advanced signal processing techniques. MCSA is a non-invasive and cost-effective technique that can detect motor faults in real-time without requiring expensive equipment or disassembly of the motor. In this study, the researchers tested the proposed diagnostic technology, which relies on a power feature. The power feature is calculated as the integrated power within a specific frequency range, centered around the fundamental harmonic of the supply frequency. The purpose of the study is to evaluate for the first time the effectiveness of the proposed diagnostic technology for the diagnosis of a tracking of a belt conveyor. The proposed technology’s effectiveness is assessed using current signals that are obtained for two different scenarios: the normal belt tracking, and a belt mis-tracking under two different loads of a belt conveyor system. The study’s findings indicate that the proposed technology has a high level of diagnostic effectiveness when used for belt mis-tracking. Therefore, it is feasible to recommend this technology for diagnosing tracking issues in belt conveyors.

Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis

In rotary-percussion drilling, the impact frequency is a crucial variable that is closely linked to operational factors that determine the efficacy of the drilling process, such as the rate of penetration, bit wear, and rock mass characteristics. Typical identification methods rely on complex simulation models or the analysis of different sensor signals installed on specially adapted setups, which are difficult to be implemented in the field. This paper presents a novel study where the impact frequency is identified by motor current signature analysis (MCSA) applied to an induction motor driving a DTH drilling setup. The analysis of the case study begins with the definition of characteristic drilling stages where the pressure and sound signals allow the detection of an impact frequency of 14.10 Hz, which is then used as a reference to validate three MCSA identification approaches. As a result of the analysis, the envelope approach is the most robust for nearly real-time implementations considering its simplicity and range of coverage. Experimental results provide evidence about the feasibility of the proposed MCSA methods to be integrated into Measurement-While-Drilling (MWD) systems to improve drilling condition monitoring and rock mass characterization.

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

Abstract 6539: Application of optical genome mapping to identify samples with homologous recombination deficiency

Abstract Certain cancer treatments, such as the poly (ADP-ribos) polymerase (PARP) inhibitors, have been shown to be effective in killing cancer cells exhibiting genome instability signatures indicative of homologous recombination deficiency (HRD). Hence, these signatures are used as biomarkers to inform treatment decisions and prognosis. There are three measurements of HRD signatures commonly employed: loss of heterozygosity (HRD-LOH), telomeric allelic imbalance (TAI) and large-scale state transition (LST). It has been shown that combining all three scores can better determine the HRD phenotype, leading to a higher clinical impact. Yet current HRD signature tests, used to estimate HRD, have low negative predictive value, and one possible reason is that current genomic technologies lack sensitivity to capture the full extent of somatic genomic rearrangements. Here, optical genome mapping (OGM) was used to detect large structural variants (SVs) and calculate a HRD score. OGM captures high molecular weight DNA to call SVs by aligning these molecules to the public reference genome. OGM can comprehensively detect insertions and deletions >5kbp, inversions, interchromosomal translocations, plus large interstitial copy number variation (CNV) and aneusomies. Subsequently, an automated script was developed to compute the HRD score, which is the summation of the three HRD signatures: HRD-LOH, the number of regions with a loss >15 Mbp but shorter than the whole chromosome; TAI, the number of regions of gain and loss >10Mbp that extend to a subtelomere but do not cross the centromere; and LST, the number of chromosomal breakpoints whose SV size >10Mb but not the whole chromosome. We applied this script on 20 samples of solid tumors and myeloid neoplasms, and the automated scores are concordant with expertly curated scores, confirming the validity of the calculation. We believe that OGM data combined with this automated analysis of HRD signatures is much more sensitive and accurate for detection of HRD signatures and that this will enable more precise prediction of drug response for multiple tumor types. Citation Format: Andy Wing Chun Pang, Kelsea Chang, Nikhil Sahajpal, Daniel Saul, Ravindra Kolhe, Alka Chaubey, Alex Hastie. Application of optical genome mapping to identify samples with homologous recombination deficiency [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6539.

Building Applications and Developing Digital Signature Devices based on the Falcon Post-Quantum Digital Signature Scheme

Falcon is an efficient and secure postquantum signature scheme for services based on quantum computing. It employs the hash-and-sign approach in conjunction with the Gentry, Peikert, and Vaikuntanathan (GPV) framework on Number Theory Research Unit (NTRU) lattices. This study evaluated the operation procedure and the capacity to run the Falcon scheme using a key length of 1024 bits on different hardware and software platforms, such as personal computers and Raspberry Pi 4 and Windows, Ubuntu, and Android operating systems. The following results were obtained: file sizes ranged from 30 to 5449268 KB, digital signature times ranged from 50 to 19500ms, and signature verification times ranged from 14 to 19000ms. The results show that the Falcon post-quantum signature scheme works stably and ensures execution speed on different platforms, similar to current digital signature schemes.

1-b Delta-Sigma ADC-Based Power Side-Channel Attack Detection Sensor

Countermeasures against power side-channel attack (SCA) range from algorithmic modifications to modifying the power consumption characteristics of the device by the inclusion of noise sources or suppressing the current signature. However, these techniques are, in most cases, passive protection techniques and come with the unacceptable area and power overheads for resource-constrained devices. In this letter, we propose a power SCA detection sensor based on a 1-b delta-sigma analog-to-digital converter, which allows for detection of an ongoing attack within as fast as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6.4\,\mu \text{s}$</tex-math></inline-formula> , with immunity to power supply noise, at an area overhead of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq 8\%$</tex-math></inline-formula> for advanced encryption standard (AES)-256, while reducing the power overhead by 66% compared to the current state-of-the-art.

Modelling and diagnosis of faults in simple bevel gear train

Gears are a fundamental component of spinning and rotating machinery that transmits power. Different loads and speeds can be applied to gear systems. Faults in gears can lead to breakdown of machines. Therefore, it is essential to keep an eye on the condition of the gears in order to avoid machine failure. Vibration, current and acoustic signature analysis are some of the fundamental methods for monitoring the state of different machineries. Modelling of a system (a virtual prototype) can be used to create enormous amounts of data with different system variables. It can replace the cost and labor involved in experimental analysis. In the present work, two models of a simple bevel gear train are taken into consideration to analyze the response of the system under wear and tear in time as well as frequency domain. The first model is a multi-body dynamics model, developed in CAD based multi-body simulation tool MBS ADAMS and the second model is a six degrees of freedom mathematical model developed in block diagram based Matlab-Simulink environment. In modelling, six different test cases of a simple gear train with healthy pinion, pinion with 20% broken teeth, 40% broken teeth, 60% broken teeth, 80% broken teeth on one tooth and one completely missing tooth are taken for consideration to study the system responses. Analysing the response of the systems, it can be inferred that, the faulty simple gear train (gears with greater amount of wear and tear) is having higher amplitude of vibration compared to that of healthy simple bevel gear train. The amplitude of vibration increases with the wear rate of tooth of the gear. In envelope spectrum, the amplitude of peaks at gear mesh frequencies is more for faulty gear train compared to the healthy bevel gear train which shows the manifestation of faults.

Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis

For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, and compared for conveyor belt mis-tracking diagnosis. The proposed technologies are based on three higher-order spectral diagnostic features: bicoherence, tricoherence, and the cross-correlation of spectral moduli of order 3 (CCSM3). The investigation of the proposed technologies via comprehensive experiments has shown that technology based on the CCSM3 is highly effective for diagnosing a conveyor belt mis-tracking via MCSA.

Provable Secure Attribute-Based Proxy Signature Over Lattice Small Integer Solution Problem in Random Oracle Model

Current proxy signature schemes are mostly identity-based proxy signatures that distinguish users by identity. This signature method faces some problems, such as identity information leakage and single access control. Attribute-based proxy signature (ABPS) divides the signer’s identity information into a collection of attributes; thus, users’ identity information can be protected and access control can become fine-grained. With the development of quantum computers, the security of signature schemes based on traditional number theory problems is under threat. Therefore, we construct a new attribute-based proxy signature scheme on a lattice that can resist quantum attacks. This scheme has the properties of both attribute-based signatures and proxy signatures, i.e., fine-grained access control and strong undeniability properties. Moreover, based on the small integer solution problem (SIS), our scheme is provably secure in the random oracle model and protects the proxy signer in the adaptive security model.